Breaking silos with the Digital Factory framework

Until Napoleon’s army came across a large slab of rock buried under the foundations of a building in the Nile Delta, in 1799, nobody knew how to read ancient Egyptian hieroglyphs. Thanks to the discovery of the Rosetta Stone, as the rock came to be known, scholars were able to use the ancient Greek inscription to decipher the hieroglyphs carved into the same slab. Something similar is happening nowadays in smart factories, where machines, which often “speak” different languages, are relying on the digital equivalents of the Rosetta Stone to understand and share information.

Simulation of use of digital twin technology for car manufacturing at the Siemen's stand, Hannover Messe fair 2018 (Photo: Alexander Tolstykh / Shutterstock.com)

Information is the lifeblood of modern manufacturing. Smart factories use technologies such as the Internet of Things (IoT) to connect intelligent machines and systems in order to enable a real-time exchange of information. This facilitates the vertical integration of the departments in an organization, as well as the horizontal integration of business partners across the entire value chain. In other words, smart manufacturing achieves efficiencies by integrating data from multiple technical systems across domains, hierarchies and geographic boundaries.

Smart manufacturing relies on an efficient exchange of data

In smart factories, digital twins and other core technologies that act as a bridge between the physical and digital world rely on an efficient exchange of data between different departments, factories and even enterprises. There are digital replicas of entire manufacturing plants that are identical in every detail to the physical factories, including all the machinery, production lines, buildings and ventilation systems, for example. The digital twin is used to plan production processes and to programme machines, as well as to design products and test them. As soon as there is an efficient virtual model and all the bugs have been ironed out, the physical factories begin production. The technology allows operators to understand in real time how the environment and their machines influence a product’s tolerances, stresses and design.

A significant challenge for smart factories is that different applications often require custom implementations in order to make it possible for them to understand and share the same data. It is a bit like the Monty Python sketch with fighter pilots struggling to understand each other’s highly idiosyncratic use of language, except that this is no laughing matter, as there are major cost implications.

Traditionally, this information has been distributed in different formats, including drawings, lists and data sheets,” explains IEC expert Thomas Hadlich. “It is presented in different structures and identified differently, for example using different denominations for the same assets or for the same data points.  It means the same data must be inputted multiple times and, worst of all, the latest information updated in one engineering tool is not automatically reflected in the same data in another engineering tool.”

A digital Rosetta Stone

The solution is to provide the different departments or enterprises involved with a digital Rosetta Stone: a common base for describing the meaning of the data that enables them to share the latest and most accurate information. That is the idea behind the international standard IEC 62832. The Digital Factory framework standard, which is currently being updated, provides a common reference for the digitization of data related to production systems. It sets out common rules for utilizing data based on computer-understandable attributes and classifications.

The Digital Factory framework is based on an existing standard, IEC 61360-2. It defines a common data dictionary (IEC CDD) for providing classifications and metadata definitions that describe products in an unambiguous way to support procurement of electrotechnical products. Companies use the definitions to provide contextually rich specifications that enable interested customers to understand the characteristics of a product. The Digital Factory framework simply applies this approach to system engineering workflows. It uses dictionaries to describe data in a way that is understandable anywhere in the world. The fact that it is an international standard means that enterprises around the world are able to develop interoperable software more easily and use data collaboratively.

The Digital Factory framework standard

Data dictionaries enable interoperability for exchanging, aggregating and analyzing data from machines that traditionally operated within individual vertical silos, where they would transfer data to industrial automation and control systems. They also facilitate the exchange of information that must take place before a supplier can provide a component to another company. The Digital Factory framework adds value by making it easier to provide quality data not only for monitoring the production, but also about the services.

The Digital Factory framework standard, IEC 62832, has three parts:

  • 62832-1:2020 PRV
    Industrial-process measurement, control and automation – Digital Factory framework – Part 1: General principles
  • IEC 62832-2:2020 PRV
    Industrial-process measurement, control and automation – Digital Factory framework – Part 2: Model elements
  • IEC 62832-3:2020 PRV
    Industrial-process measurement, control and automation – Digital Factory framework – Part 3: Application of Digital Factory for life cycle management of production systems

Download the information document for a more in-depth look at the IEC 62832 and the Digital Factory framework.